SOTAVerified

Transfer Learning

Transfer Learning is a machine learning technique where a model trained on one task is re-purposed and fine-tuned for a related, but different task. The idea behind transfer learning is to leverage the knowledge learned from a pre-trained model to solve a new, but related problem. This can be useful in situations where there is limited data available to train a new model from scratch, or when the new task is similar enough to the original task that the pre-trained model can be adapted to the new problem with only minor modifications.

( Image credit: Subodh Malgonde )

Papers

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Showing 9351–9375 of 10307 papers

TitleStatusHype
Conditional Bures Metric for Domain Adaptation—0
Conditional computation in neural networks: principles and research trends—0
Conditional Data Synthesis Augmentation—0
RCKD: Response-Based Cross-Task Knowledge Distillation for Pathological Image Analysis—0
Conditional Electrocardiogram Generation Using Hierarchical Variational Autoencoders—0
Biologically inspired sleep algorithm for artificial neural networks—0
Conditional Neural Processes for Molecules—0
RCMNet: A deep learning model assists CAR-T therapy for leukemia—0
Conditional Loss and Deep Euler Scheme for Time Series Generation—0
Reacting like Humans: Incorporating Intrinsic Human Behaviors into NAO through Sound-Based Reactions to Fearful and Shocking Events for Enhanced Sociability—0
BioIE: Biomedical Information Extraction with Multi-head Attention Enhanced Graph Convolutional Network—0
Confidence Aware Neural Networks for Skin Cancer Detection—0
Confidence-Aware Subject-to-Subject Transfer Learning for Brain-Computer Interface—0
BioBERTpt - A Portuguese Neural Language Model for Clinical Named Entity Recognition—0
Confidence Estimation in Unsupervised Deep Change Vector Analysis—0
Confidence-Nets: A Step Towards better Prediction Intervals for regression Neural Networks on small datasets—0
Confidence Preserving Machine for Facial Action Unit Detection—0
BioAMA: Towards an End to End BioMedical Question Answering System—0
Conformal Prediction Under Generalized Covariate Shift with Posterior Drift—0
Conjuring Positive Pairs for Efficient Unification of Representation Learning and Image Synthesis—0
BiNet: Degraded-Manuscript Binarization in Diverse Document Textures and Layouts using Deep Encoder-Decoder Networks—0
Connecting the Dots between Audio and Text without Parallel Data through Visual Knowledge Transfer—0
Binary Paragraph Vectors—0
Consensus-Based Transfer Linear Support Vector Machines for Decentralized Multi-Task Multi-Agent Learning—0
Adaptive Prototype Knowledge Transfer for Federated Learning with Mixed Modalities and Heterogeneous Tasks—0
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All datasetsOffice-Home100 sleep nights of 8 caregiversBanglaLekha Isolated DatasetCOCO70KITTI Object Tracking Evaluation 2012Retinal Fundus MultiDisease Image Dataset (RFMiD)

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1APCLIPAccuracy84.2—Unverified
2DFA-ENTAccuracy69.2—Unverified
3DFA-SAFNAccuracy69.1—Unverified
4EasyTLAccuracy63.3—Unverified
5MEDAAccuracy60.3—Unverified
#ModelMetricClaimedVerifiedStatus
1CNN10-20% Mask PSNR3.23—Unverified
#ModelMetricClaimedVerifiedStatus
1Chatterjee, Dutta et al.[1]Accuracy96.12—Unverified
#ModelMetricClaimedVerifiedStatus
1Co-TuningAccuracy85.65—Unverified
#ModelMetricClaimedVerifiedStatus
1Physical AccessEER5.74—Unverified
#ModelMetricClaimedVerifiedStatus
1riadd.aucmediAUROC0.95—Unverified